Accurate estimation of thermal conductivity (TC) of nanofluids is essential in thermophysical studies of nanofluids. In this study, more than 3200 data of thermal conductivity of nanofluids were collected from literature to develop simple-to-use and accurate correlations for predicting relative thermal conductivity of nanofluids. The dataset includes 13 different nanofluids with temperature from −30.00 to 149.15 °C, particle size from 5.00 to 150.00 nm, particle thermal conductivity from 1.20 to 1000.00 W/mk, particle volume fractions from 0.01 to11.22%, and base fluid thermal conductivity from 0.11 to 0.69 W/mk. Group method of data handling (GMDH) and gene expression programming (GEP) as two white-box powerful models were used for modeling. The results of proposed models were compared to 23 well-known theoretical and empirical models. The statistical and graphical results showed that the proposed models are more precise and reliable than the existing ones in literature. The GMDH model showed a better performance compared to GEP, and could predict all data with an average absolute relative error of 2.27% in training and 2.44% in testing data set. In addition, it was found that the proposed models could capture the physically expected trends with variation of temperature, size of nanoparticles, and volume fraction. The sensitivity analysis illustrated that temperature has the highest effect on the relative TC followed by base fluid TC and particle volume fraction